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 *
 *  Copyright NumFOCUS
 *
 *  Licensed under the Apache License, Version 2.0 (the "License");
 *  you may not use this file except in compliance with the License.
 *  You may obtain a copy of the License at
 *
 *         https://www.apache.org/licenses/LICENSE-2.0.txt
 *
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 *  distributed under the License is distributed on an "AS IS" BASIS,
 *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *  See the License for the specific language governing permissions and
 *  limitations under the License.
 *
 *=========================================================================*/

//
// This is an example of the itk::BayesianClassifierInitializationImageFilter.
// The example's goal is to serve as an initializer for the
// BayesianClassifier.cxx example also found in this directory.
//
// This example takes an input image (to be classified) and generates
// membership images. The membership images determine the degree to which each
// pixel belongs to a class.
//
// The membership image generated by the filter is an
// itk::VectorImage, (with pixels organized as follows: For a 2D image,
// its essentially a 3D array on file with DataType[y][x][c] where c is the
// number of classes and DataType is the template parameter of the filter
// (defaults to float). For a 3D image, it will be organized as
// Datatype[z][y][x][c])
//
// The example also optionally takes in two more arguments, as a convenience
// to the user. These arguments extract the specified component 'c' from the
// membership image and rescale, so the user can fire up a typical image
// viewer and see the relative pixel memberships to class 'c'.
//
// Example args:
//   BrainProtonDensitySlice.png Memberships.mhd 4  2 Class2.png
//
// Here Memberships.mhd will be a 2x2x4 image containing pixel memberships
// Class2.png shows pixel memberships to the third class, (rescaled for
// display)
//
// Notes:
//   The default behaviour of the filter is to generate memberships by
//   centering
// gaussian density functions around K-means of the pixel intensities in the
// image. The filter allows you to specify your own membership functions as
// well.
//

#include "itkImage.h"
#include "itkBayesianClassifierInitializationImageFilter.h"
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkRescaleIntensityImageFilter.h"
#include "itkImageRegionConstIterator.h"

int
main(int argc, char * argv[])
{

  constexpr unsigned int Dimension = 2;
  if (argc < 4)
  {
    std::cerr
      << "Usage arguments: InputImage MembershipImage numberOfClasses "
         "[componentToExtract ExtractedImage]"
      << std::endl;
    std::cerr
      << "  The MembershipImage image written is a VectorImage, ( an image "
         "with multiple components ) ";
    std::cerr << "Given that most viewers can't see vector images, we will "
                 "optionally "
                 "extract a component and ";
    std::cerr << "write it out as a scalar image as well." << std::endl;
    return EXIT_FAILURE;
  }

  try
  {
    using ImageType = itk::Image<unsigned char, Dimension>;
    using BayesianInitializerType =
      itk::BayesianClassifierInitializationImageFilter<ImageType>;
    auto bayesianInitializer = BayesianInitializerType::New();

    auto input = itk::ReadImage<ImageType>(argv[1]);

    bayesianInitializer->SetInput(input);
    bayesianInitializer->SetNumberOfClasses(std::stoi(argv[3]));

    // TODO add test where we specify membership functions

    bayesianInitializer->Update();

    itk::WriteImage(bayesianInitializer->GetOutput(), argv[2]);

    if (argv[4] && argv[5])
    {
      using MembershipImageType = BayesianInitializerType::OutputImageType;
      using ExtractedComponentImageType =
        itk::Image<MembershipImageType::InternalPixelType, Dimension>;
      auto extractedComponentImage = ExtractedComponentImageType::New();
      extractedComponentImage->CopyInformation(
        bayesianInitializer->GetOutput());
      extractedComponentImage->SetBufferedRegion(
        bayesianInitializer->GetOutput()->GetBufferedRegion());
      extractedComponentImage->SetRequestedRegion(
        bayesianInitializer->GetOutput()->GetRequestedRegion());
      extractedComponentImage->Allocate();
      using ConstIteratorType =
        itk::ImageRegionConstIterator<MembershipImageType>;
      using IteratorType =
        itk::ImageRegionIterator<ExtractedComponentImageType>;
      ConstIteratorType cit(
        bayesianInitializer->GetOutput(),
        bayesianInitializer->GetOutput()->GetBufferedRegion());
      IteratorType it(extractedComponentImage,
                      extractedComponentImage->GetLargestPossibleRegion());

      const unsigned int componentToExtract = std::stoi(argv[4]);
      cit.GoToBegin();
      it.GoToBegin();
      while (!cit.IsAtEnd())
      {
        it.Set(cit.Get()[componentToExtract]);
        ++it;
        ++cit;
      }

      // Write out the rescaled extracted component
      using OutputImageType = itk::Image<unsigned char, Dimension>;
      using RescalerType =
        itk::RescaleIntensityImageFilter<ExtractedComponentImageType,
                                         OutputImageType>;
      auto rescaler = RescalerType::New();
      rescaler->SetInput(extractedComponentImage);
      rescaler->SetOutputMinimum(0);
      rescaler->SetOutputMaximum(255);
      itk::WriteImage(rescaler->GetOutput(), argv[5]);
    }
  }
  catch (const itk::ExceptionObject & excp)
  {
    std::cerr << "ITK exception caught:\n" << excp << std::endl;
    return EXIT_FAILURE;
  }

  return EXIT_SUCCESS;
}
